The International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research. Their extratropical transition also has a major impact on the skill of midlatitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extratropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally, the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill.

For the next generation of the World Area Forecast System (WAFS), the global Graphical Turbulence Guidance (G-GTG) has been developed using global numerical weather prediction (NWP) model outputs as an input to compute a set of turbulence diagnostics, identifying strong spatial gradients of meteorological variables associated with clear-air turbulence (CAT) and mountain-wave turbulence (MWT). The G-GTG provides an atmospheric turbulence intensity metric of energy dissipation rate (EDR) to the 1/3 power (m2/3 s–1), which is the International Civil Aviation Organization (ICAO) standard for aircraft reporting. Deterministic CAT and MWT EDR forecasts are derived from ensembles of calibrated multiple CAT and MWT diagnostics, respectively, with the final forecast provided by the gridpoint-by-gridpoint maximum of the CAT and MWT ensemble means. In addition, a probabilistic EDR forecast is produced by the percentage agreement of the individual CAT and MWT diagnostics that exceed a certain EDR threshold for turbulence (i.e., multidiagnostic ensemble). Objective evaluations of the G-GTG against global in situ EDR measurement data show that both deterministic and probabilistic G-GTG significantly improve the current WAFS CAT product, mainly because the G-GTG takes into account turbulence from various sources related to CAT and MWT. The probabilistic G-GTG forecast is more reliable at predicting light-or-greater (EDR > 0.15)- than moderate-or-greater (EDR > 0.22)-level turbulence, although it suffers from overforecasting. This will be improved in the future when we use this methodology with NWP ensembles and more observation data will be available for calibration. We expect that the new G-GTG forecasts will be beneficial to aviation users globally.

Turbulence is a major source of weather-related aviation incidents. There are many different indicators used to try and predict where turbulence is likely to occur. The indicators are derived from deterministic models although they are often quoted as probabilities. This paper proposes the use of ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS) to produce a probabilistic indicator of wind shear and convectively induced moderate or greater turbulence. An objective verification scheme using high-resolution automated aircraft observations from the Global Aircraft Data Set is used to compare the skill with the routinely available World Area Forecast Centre gridded turbulence forecasts. The forecasts are assessed globally over a 12 month period from November 2010 to October 2011 looking at the skill, reliability and economic value of the forecasts.

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Changes in the North Atlantic Oscillation (NAO) heavily influence the weather across the UK and the rest of Europe. Due to an incorrect representation of the polar jet stream and its associated physical processes, it is reasonable to believe that errors in numerical weather prediction models may also depend on the prevailing behaviour of the NAO. To address this, information regarding the NAO is incorporated into statistical post-processing methods through a regime-dependent mixture model, which is then applied to wind speed forecasts from the Met Office's global ensemble prediction system, MOGREPS-G. The mixture model offers substantial improvements upon conventional post-processing methods when the local wind speed depends strongly on the NAO, but the additional complexity of the model can hinder forecast performance otherwise. A measure of regime dependency is thus defined that can be used to differentiate between situations when the numerical model output is, and is not, expected to benefit from regime-dependent post-processing. Implementing the regime-dependent mixture model only when this measure exceeds a certain threshold is found to further improve predictive performance, while also producing more accurate forecasts of extreme wind speeds.

<p>Changes in the North Atlantic Oscillation (NAO) heavily influence the weather across the UK and the rest of Europe. Due to an imperfect reconstruction of the polar jet stream and associated pressure systems, there is reason to believe that errors in numerical weather prediction models may also depend on the prevailing behaviour of the NAO. To address this, information regarding the NAO is incorporated into statistical post-processing methods through a regime-dependent mixture model, which is then applied to wind speed forecasts from the Met Office's global ensemble prediction system, MOGREPS-G. The mixture model offers substantial improvements upon conventional post-processing methods when the wind speed depends strongly on the NAO, but the additional complexity of the model can hinder forecast performance in other instances. A measure of regime-dependency is thus defined that can be used to differentiate between situations when the numerical model output is, and is not, expected to benefit from regime-dependent post-processing. Implementing the regime-dependent mixture model only when this measure exceeds a certain threshold is found to further improve predictive performance, while also producing more accurate forecasts of extreme wind speeds.</p>

When statistically post-processing temperature forecasts, it is almost always assumed that the future temperature follows a Gaussian distribution conditional on the output of an ensemble prediction system. Recent studies, however, have demonstrated that it can at times be beneficial to employ alternative parametric families when post-processing temperature forecasts, that are either asymmetric or heavier-tailed than the normal distribution. In this article, we compare choices of the parametric distribution used within the Ensemble Model Output Statistics (EMOS) framework to statistically post-process 2m temperature forecast fields generated by the Met Office’s regional, convection-permitting ensemble prediction system, MOGREPS-UK. Specifically, we study the normal, logistic and skew-logistic distributions. A flexible alternative is also introduced that first applies a Yeo-Johnson transformation to the temperature forecasts prior to post-processing, so that they more readily conform to the assumptions made by established post-processing methods. It is found that accounting for the skewness of temperature when post-processing can enhance the performance of the resulting forecast field, particularly during summer and winter and in mountainous regions.

Multiple studies have considered whether increased anthropogenic CO2 will affect the wind speeds and turbulence associated with the winter North Atlantic polar‐front jet stream in the upper atmosphere. Key questions are whether any effects can already be seen and, if so, can they be seen independent of computer models of the atmosphere. In this study we use two reanalyses, NCEP/NCAR and the ECMWF ERA5, and two large observational archives, AMDAR/ACARS and the Global Aircraft Data Set (GADS), to try to answer these questions for the period 2002–2020 when automated aircraft observations were plentiful over the North Atlantic. We focus on eastbound, New York to London, flights. No significant increase appears in reanalyses during the last roughly 40 years (1979–2020) which is our best estimate for the modern satellite era. In contrast, for the last roughly 20 years (2002–2020) both the ERA5 reanalysis (2.5% per year) and the GADS archive (1.2% to 1.4% per year) show a statistically significant rise in the wind speed in the North Atlantic jet streak exit region. These results must be considered in the context of atmospheric oscillations, changes to the North Atlantic Track System (NATS), and the effects of aircraft step climbs. We estimate that up to 0.5% of the rise may be due to improvements in the NATS operations and an unknown additional amount may be due to the substantial increase in automated aircraft observations starting in 1997. We also examine the impact of aircraft observations on one's confidence in drawing conclusions from secular changes in the reanalyses. For turbulence, the Light turbulence trends are not statistically significant. Our confidence in the turbulence results is more limited since these observations reflect medium‐term changes in tactical and strategic aircraft operational procedures as well as the underlying prevalence of turbulence.